32 citations · 58 across the 37 of their papers we have counts for
14 papers · 1 filter
HINTS: Extraction of Human Insights from Time-Series Without External Sources
Sheo Yon Jhin, Noseong Park
Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series…
How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts
Sumin Park, Noseong Park
Finding the optimal configuration of Sparse Mixture-ofExperts (SMoE) that maximizes semantic differentiation among experts is essential for exploiting the full potential of MoE arc…
Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
Jeongwhan Choi, Woosung Kang, Minseo Kim +2
Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN for tabular data and…
TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models
Haksoo Lim, Jaehoon Lee, Sewon Park +2
Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data s…
Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs
Jeongwhan Choi, Seungjun Park, Sumin Park +2
Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, but often struggle to balance local and global information. While graph Transform…
Learning Low Rank Neural Representations of Hyperbolic Wave Dynamics from Data
Woojin Cho, Kookjin Lee, Noseong Park +2
We present a data-driven dimensionality reduction method that is well-suited for physics-based data representing hyperbolic wave propagation. The method utilizes a specialized neur…
TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
Yehjin Shin, Jeongwhan Choi, Seojin Kim +1
Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models co…
Efficient Cell Painting Image Representation Learning via Cross-Well Aligned Masked Siamese Network
Pin-Jui Huang, Yu-Hsuan Liao, SooHeon Kim +3
Computational models that predict cellular phenotypic responses to chemical and genetic perturbations can accelerate drug discovery by prioritizing therapeutic hypotheses and reduc…
PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling
Minju Jo, Woojin Cho, Uvini Balasuriya Mudiyanselage +3
Scientific machine learning often involves representing complex solution fields that exhibit high-frequency features such as sharp transitions, fine-scale oscillations, and localiz…
Neural Functions for Learning Periodic Signal
Woojin Cho, Minju Jo, Kookjin Lee +1
As function approximators, deep neural networks have served as an effective tool to represent various signal types. Recent approaches utilize multi-layer perceptrons (MLPs) to lear…
Learning Advanced Self-Attention for Linear Transformers in the Singular Value Domain
Hyowon Wi, Jeongwhan Choi, Noseong Park
Transformers have demonstrated remarkable performance across diverse domains. The key component of Transformers is self-attention, which learns the relationship between any two tok…
Possibility for Proactive Anomaly Detection
Jinsung Jeon, Jaehyeon Park, Sewon Park +3
Time-series anomaly detection, which detects errors and failures in a workflow, is one of the most important topics in real-world applications. The purpose of time-series anomaly d…
PIORF: Physics-Informed Ollivier-Ricci Flow for Long-Range Interactions in Mesh Graph Neural Networks
Youn-Yeol Yu, Jeongwhan Choi, Jaehyeon Park +2
Recently, data-driven simulators based on graph neural networks have gained attention in modeling physical systems on unstructured meshes. However, they struggle with long-range de…
Unveiling the Potential of Superexpressive Networks in Implicit Neural Representations
Uvini Balasuriya Mudiyanselage, Woojin Cho, Minju Jo +2
In this study, we examine the potential of one of the ``superexpressive'' networks in the context of learning neural functions for representing complex signals and performing machi…